4.3.1.1 Customer Data

Only the age can be regarded as a numerical customer data column here. As most intersting in this summary might appear the mean and the median, which both imply an average customer segment consisting of people in their late 30s.

Variable	Max	Mean	Median	Min	SD
Age	98	38.37	36	18	10.87

The following data summary shows some social data for the shop’s customer segment. Since all of the available data for the country column contains the value ‘United States’, it is highly probable that the online shop exclusively delivers customer located in the US. This was the reason for us to choose a map of the United States in order to visualize the customers’ locations later on in the plotting. Furthermore, the data clearly shows that the main customer audience targeted are women.

Variable	Top.1st	Top.2nd	Top.3rd	Top.4th	Top.5th	Others	Not.Available
City	New York: 4.53%	San Francisco: 2.05%	Stamford: 1.24%	Austin: 1.13%	Brooklyn: 0.98%	90.07%	0%
Country	United States: 100%					0%	2.83%
US.State	CA: 14.63%	NY: 14.11%	TX: 6.93%	PA: 5.8%	CT: 5.28%	53.25%	0%
Marital.Status	Married: 66.13%	Single: 22.02%	Inferred Single: 7.15%	Inferred Married: 4.7%		0%	34.98%
Gender	Female: 83.06%	Male: 16.94%				0%	44.96%
Audience	Women: 81.17%	Men: 12.5%	Children: 6.33%			0%	11.08%
Truck.Owner	False: 78.55%	True: 21.45%				0%	22.22%
RV.Owner	False: 91.5%	True: 8.5%				0%	22.22%
Motorcycle.Owner	False: 98.66%	True: 1.34%				0%	22.22%
Working.Woman	False: 68.79%	True: 31.21%				0%	22.22%
Presence.Of.Children	False: 54.66%	True: 45.34%				0%	22.22%
Speciality.Store.Retail	False: 84.12%	True: 15.88%				0%	22.22%
Oil.Retail.Activity	False: 91.8%	True: 8.2%				0%	22.22%
Bank.Retail.Activity	False: 75.44%	True: 24.56%				0%	22.22%
Finance.Retail.Activity	False: 91.69%	True: 8.31%				0%	22.22%
Miscellaneous.Retail.Activity	False: 94.88%	True: 5.12%				0%	22.22%
Upscale.Retail	False: 94.25%	True: 5.75%				0%	22.22%
Upscale.Speciality.Retail	False: 96.44%	True: 3.56%				0%	22.22%
Retail.Activity	False: 60%	True: 40%				0%	22.22%

4.3.1.2 Product Data

The selected columns belonging to the product information section show only factorial values. The statistical overview for the product data reveals that most of the sold articles are replenishable. The strongest brand in the current orders is American Essential, which seems to manufacture its articles by itself. Important is to recognize that the presented data is biased in a way: Because the given dataset shows only ordered products, it can be assumed that the popularity of articles distorts all percentual information referring to the products. Thus, we can not make any assumptions referring to the original product palette the store offers.

Variable	Top.1st	Top.2nd	Top.3rd	Top.4th	Top.5th	Others	Not.Available
StockType	Replenishable: 69.58%	Seasonal 1: 23.08%	Replenishment: 5.14%	Seasonal 1*: 1.83%	Seasonal 2: 0.24%	0%	14.72%
Manufacturer	American Essentials: 20.91%	Ridgeview: 16.64%	HAN: 13.16%	Donna Karan Company: 10.85%	HOSO: 10.67%	Peneco: 8.98%	0%
BrandName	AME: 22.07%	HOSO: 11.26%	ELT: 10.81%	Silk Reflections: 9.35%	DAN: 7.92%	27.51%	11.08%

4.3.1.3 Payment Data

When it comes to the data concerning the used payment methods, there are only factorial columns as well. The most used credit card is by far the VISA card. Furthermore almost a fifth of the customers uses a premium card. From this information it could be deduced how wealthy the customer segment is by comparing the ratio of premium cards to the one in the whole population.

Variable	Top.1st	Top.2nd	Top.3rd	Top.4th	Top.5th	Others	Not.Available
Order.Credit.Card.Brand	VISA: 59.94%	MC: 25.43%	AMEX: 14.31%	DISC: 0.31%	NA	0%	16.71%
Bank.Card.Holder	True: 86.57%	False: 13.43%			NA	0%	22.22%
Gas.Card.Holder	True: 75.81%	False: 24.19%			NA	0%	22.22%
Upscale.Card.Holder	True: 54.1%	False: 45.9%			NA	0%	22.22%
Unknown.Card.Type	False: 56.18%	True: 43.82%			NA	0%	22.22%
TE.Card.Holder	False: 89.42%	True: 10.58%			NA	0%	22.22%
Premium.Card.Holder	False: 75.88%	True: 24.12%			NA	0%	22.22%
New.Bank.Card	False: 99.55%	True: 0.45%			NA	0%	22.22%

4.3.1.4 Order Process Data

The numerical data for the order process shows that a customer usally buys one product per order. Also the order line amount implies that the store offers rather inexpensive articles. Furthermore, the minimum value for both the order line quantity and the order line amount is negative, which hints to the assumption of eather the order data containing returns as well or having errors in it. Through the discount amount it is possible to state that the store offers a maximum of a 50% price reduction for the given time period. Also the buyed articles got by average a discount of about 9%. Again it can be assumed that this data is not representative for the shop’s offer in general, because it is probable that articles with a higher discount are bought more often.

Variable	Max	Mean	Median	Min	SD
Order.Line.Quantity	18	1.31	1.0	-2	0.95
Order.Line.Unit.List.Price	72	9.26	7.5	0	6.46
Order.Line.Amount	234	11.62	10.0	-40	11.51
Order.Line.Hour.of.Day	23	13.04	13.0	0	5.29
Order.Discount.Amount	50	8.82	10.0	0	9.98

The factors for the order process demonstrate that the ‘FRIEND’ discount is used most often and in the majority of the orders. (At this point it would be relevant for the interpretation to know for whom and under which circumstances this discount is given.) Also the weekday summary could be relevant for sales purposes, by example for finding out the most successfull time for showing ads to possible customers.

Variable	Top.1st	Top.2nd	Top.3rd	Top.4th	Top.5th	Others	Not.Available
Spend.Over..12.Per.Order.On.Average	False: 64.04%	True: 35.96%				0%	0%
Order.Line.Day.of.Week	Wednesday: 26.96%	Tuesday: 17.72%	Thursday: 17.37%	Friday: 16.48%	Saturday: 8.43%	13.04%	0%
Order.Promotion.Code	FRIEND: 82.09%	SPRING: 2.14%	MARCH1: 1.92%	FREE: 1.39%	4128003160593466: 1.13%	0%	23.15%

4.3.2.1 Customer Data

The age is very similar to that of the Order Data, averaging at 37.58, with a median of 36. This shows that buyers do not differ to viewers in respect to their age.

Variable	Max	Mean	Median	Min	SD
Age	86	37.58	36	18	10.71

The same can be said in regards to the location compared to the Order Data, in that there are only minor differences. Additionally, the gender distribution is very similar, heavily pointing out the shop’s main target group are women.
For all details see the full customer data table.

Variable	Top.1st	Top.2nd	Top.3rd	Top.4th	Top.5th	Others	Not.Available
City	San Francisco: 2.31%	New York: 2.18%	Chicago: 1.35%	Stamford: 1.09%	Dallas: 0.71%	0%	96.92%
US.State	CA: 13.28%	NY: 11.1%	TX: 5.97%	PA: 5.39%	IL: 4.68%	0%	96.92%
Marital.Status	Married: 61.8%	Single: 24.7%	Inferred Married: 7%	Inferred Single: 6.5%		0%	98.02%
Gender	Female: 83.33%	Male: 16.67%				0%	98.43%
Audience	Women: 85.44%	Children: 10.27%	Men: 4.29%			0%	98.25%
Truck.Owner	False: 77.84%	True: 22.16%				0%	97.72%
RV.Owner	False: 91.17%	True: 8.83%				0%	97.72%
Motorcycle.Owner	False: 98.79%	True: 1.21%				0%	97.72%
Working.Woman	False: 65.54%	True: 34.46%				0%	97.72%
Presence.Of.Children	False: 50.39%	True: 49.61%				0%	97.72%
Speciality.Store.Retail	False: 84.59%	True: 15.41%				0%	97.72%
Oil.Retail.Activity	False: 90.65%	True: 9.35%				0%	97.72%
Bank.Retail.Activity	False: 77.58%	True: 22.42%				0%	97.72%
Finance.Retail.Activity	False: 90.3%	True: 9.7%				0%	97.72%
Miscellaneous.Retail.Activity	False: 94.37%	True: 5.63%				0%	97.72%
Upscale.Retail	False: 94.37%	True: 5.63%				0%	97.72%
Upscale.Speciality.Retail	False: 96.36%	True: 3.64%				0%	97.72%
Retail.Activity	False: 64.33%	True: 35.67%				0%	97.72%

4.3.2.2 Product Data

In the clickstream dataset most of the products are only viewed and not bought. That leads to some interesting distinctions to the Order Product Data. Seasonal 1* is 10% higher than in the Order Product Data and decreasing the replenishable part with the same amount. Donna Karan is leading the top manufacturer and the top brands, but is closely followed by its competitors. However, when compared to the Order Product Data this company is only in the 4th spot and its product not at all in the top 5, leading to the assumption that customers do not inherently buy that what is most viewed.
For all details see the full products data table.

Variable	Top.1st	Top.2nd	Top.3rd	Top.4th	Top.5th	Others	Not.Available
StockType	Replenishable: 60.93%	Seasonal 1: 23.75%	Seasonal 1*: 11.25%	Seasonal 2: 2.07%	Replenishment: 1.83%	0%	80.3%
Manufacturer	Donna Karan Company: 10.79%	Peneco: 9.08%	HAN: 8.66%	Kneipp: 6.61%	Paul Lavitt Mills Inc.: 6.54%	0%	80.12%
BrandName	DKNY: 9.99%	Silk Reflections: 9.21%	ORO: 8.85%	HPK: 7.22%	AME: 7.13%	0%	86.16%

4.3.2.3 Payment Data

The payment data from the clickstream set is not as reliable, as this kind of information should only be available when purchases were made and so the data should be quite similar to the Order payment data. Interestingly the data sets differ more than expected, most importantly in the amount of premium cards which is lower by about 5% in the clickstream set, and new credit cards are reported at all.

Variable	Top.1st	Top.2nd	Top.3rd	Top.4th	Top.5th	Others	Not.Available
Bank.Card.Holder	True: 81.44%	False: 18.56%	NA	NA	NA	0%	89.44%
Gas.Card.Holder	True: 68.95%	False: 31.05%	NA	NA	NA	0%	89.44%
Upscale.Card.Holder	False: 50.88%	True: 49.12%	NA	NA	NA	0%	89.44%
Unknown.Card.Type	False: 59.71%	True: 40.29%	NA	NA	NA	0%	89.44%
TE.Card.Holder	False: 90.67%	True: 9.33%	NA	NA	NA	0%	89.44%
Premium.Card.Holder	False: 79.92%	True: 20.08%	NA	NA	NA	0%	89.44%
New.Bank.Card	False: 100%		NA	NA	NA	0%	89.44%

4.3.2.4 Calculated Data

Since not all interesting data can be recorded through simply summaring the existing columns, we decided to add calculated columns. The session duration gives the time in minutes a customer spend on the website, which gets calculated through the timestamps on the first and last click of a session. The click number represents how much clicks a customer made during one session. For both these values, the max values indicate that there could be bot activity on the website.

Variable	Max	Mean	Median	Min	SD
Session.Duration	1439.983	8.88	2.55	0	74.34
Click.Number	1842.000	46.93	4.00	1	189.84

4.4.1.1 Customer Data

The customer data of the order data can be viewed from two perspectives: One way is to use every single order row for the creation of the visualizations and therby create a weighted view on the data, in which customers that have bought more products are more respected. Another possibility is to display the customer information just for every unique customer in the order data and disregard the number of orders a customer made. The following plottings will show both of this perspectives.

Firstly, we genereted some density curves for the attribute age to visualize the distribution of the customership. Also, we differentiated between the genders for this. The curves all show a fast rise in customership for the ages 20 to 40, which decreases slowly after a peak at about 35 to 40. When comparing the weighted and the normal graph, a slightly shift of the curve can be observed. This indicates that in general older people tend to order more. This is especially relevant for an older male customership, which is shown by the peak at about 55 in the male curve of the weighted density plot. Additionally, a less wide peak can be observed for the male customers, which has its maximum shortly before the age of 40. Furthermore, it should be regarded, that the gender plot shows a prozentual curve for each gender, but the ratio of customers differs by gender.

In order to show the customer location, we generated a map of the United States, which shows the cities with the highest customer numbers. From this it can be observed that the west coast of the US orders in general the most. The difference between the weighted and the other customer graph shows that the customership from the large cities like New York or San Franscisco tends to have high order numbers since the large circles at this cities disappear, when we look at the non-weighted graph.

To visualize the importance of different areas, we created a heatmap for the different US States. Here we can see that California and New York are the most important customer states. This is probably highly influenced by the big cities in this states. Here, the distorting factor of population density must be taken into account.

4.4.1.2 Product Data

For the product Data, it has to be regarded that we here visualize the orders and thereby the characteristics of more often bought products have a higher influence. Because of this, the following plots should be seen as popularity graphs of different product attributes.

The following stacked bar plot shows the stock types for the top brands and manufactures. Most of them have a replenishable assortment. The biggest brands and manufacturers seem to have mixed stock types, which contain partly seasonal products.

The lorenz curve of the products shows that only a quarter of the whole product quantity is responsible for about 75% of the ordered products. This indicates that some products have a very high popularity.

The manufacturer lorenz curve shows a real high ratio on orders for the biggest manufacturer, which leads to the assumptions that the popularity of some manufacturers is even higher than the product popularity.

The brand lorenz curve looks a little more flat, indicating a higher relevance for the manufacturer than for the brand.

4.4.1.3 Payment Data

The stacked bar plot for credit card brands shows the ratio of each brand on premium and on upscale cards. The card brand AMEX is here noticable with a relative high ratio of premium and upscale cards.

4.4.1.4 Order Process Data

For the order process data some graphs referring to the order amount and price were created. It has to be respected again that the order data can deform the graphs, by example it is imaginable that the customers tend to buy the cheaper products and therefore the average product price seems lower.

The density curve for the discount amount shows 3 peaks: The first one has a medium height and is around a discount of 0%, the second one is around the 10% mark and is pretty large, whereas the last peak is at a discount of 50% and is rather low. This might indicate that customers buy rather targeted than randomly.

A density curve reflecting the order time is shown by the next plot. As to be expected the order amount goes down through the night. During the day the ordering is relative stable with some small peaks at 10 a.m. and in the afternoon. The activity in the afternoon can be explained by the average working hours, which mostly allow people only to spend time on online shopping at the afternoon and evening.

Next, there is displayed a graph, which summarizes some information on order behaviour. From this visulalization we can learn that the online shop sells products of a low price segments, but usually receives orders contaning a rather high amount (peak at 3-15) of articles. The different history plots on the right indicate a high activity in the first half of February, which drops in the beginning of the second month half and than slowly rises again. The first half could possibly be explained through customers buying presents for the valentines day (14th February).

4.4.2.1 Click time per Day

Most of the licks are accumulated in the morning and then descending over day. The extreme spike at around 2 in the morning can be neglected as it seems to be bot activity with extremly long session times and sequences.

4.4.2.2 Click time overall

The above mentioned behavior is also visible in this graph. Overall there is more activity during the middle of months and less towards the end. Also, the seasonal component of the clickstream data set is visible very well.

4.4.2.3 Session length

While the session length on average is about 8 min, the median only is at about 2 min (see table). This is visible in the graph where the high point is also at about 2 min indicating the skewness of the time data. Most sessions last only around 10 min, which leads to the assumption that products should be displayed well as the customers do not waste much time on searching for them.